Overview

Brought to you by YData

Dataset statistics

Number of variables23
Number of observations3687
Missing cells3917
Missing cells (%)4.6%
Duplicate rows4
Duplicate rows (%)0.1%
Total size in memory2.0 MiB
Average record size in memory563.8 B

Variable types

Text3
Numeric10
Categorical10

Alerts

Dataset has 4 (0.1%) duplicate rowsDuplicates
area is highly overall correlated with builtup and 3 other fieldsHigh correlation
balcony is highly overall correlated with propertyTypeHigh correlation
bathRooms is highly overall correlated with bedRooms and 1 other fieldsHigh correlation
bedRooms is highly overall correlated with bathRooms and 2 other fieldsHigh correlation
builtup is highly overall correlated with area and 3 other fieldsHigh correlation
carpet is highly overall correlated with area and 3 other fieldsHigh correlation
floorNum is highly overall correlated with propertyTypeHigh correlation
price is highly overall correlated with bathRooms and 3 other fieldsHigh correlation
pricePerSqft is highly overall correlated with area and 5 other fieldsHigh correlation
propertyType is highly overall correlated with balcony and 2 other fieldsHigh correlation
superbuilt is highly overall correlated with area and 3 other fieldsHigh correlation
others is highly imbalanced (51.5%) Imbalance
pricePerSqft has 357 (9.7%) missing values Missing
area has 357 (9.7%) missing values Missing
superbuilt has 357 (9.7%) missing values Missing
carpet has 357 (9.7%) missing values Missing
builtup has 357 (9.7%) missing values Missing
balcony has 2120 (57.5%) missing values Missing

Reproduction

Analysis started2025-04-20 12:27:23.317087
Analysis finished2025-04-20 12:27:28.084949
Duration4.77 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct667
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size268.8 KiB
2025-04-20T17:57:28.179932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length70
Median length57
Mean length25.622457
Min length7

Characters and Unicode

Total characters94470
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique296 ?
Unique (%)8.0%

Sample

1st row palam vihar, gurgaon
2nd row m3m woodshire
3rd row central park flower valley aqua front towers
4th row sector 45, gurgaon
5th row nirvana country, gurgaon
ValueCountFrequency (%)
gurgaon 1622
 
11.2%
sector 870
 
6.0%
dlf 529
 
3.7%
phase 523
 
3.6%
the 379
 
2.6%
city 363
 
2.5%
1 345
 
2.4%
park 241
 
1.7%
sushant 218
 
1.5%
block 218
 
1.5%
Other values (628) 9130
63.2%
2025-04-20T17:57:28.354166image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18131
19.2%
a 7832
 
8.3%
r 6370
 
6.7%
e 6227
 
6.6%
o 5500
 
5.8%
s 4789
 
5.1%
t 4581
 
4.8%
g 4333
 
4.6%
n 4286
 
4.5%
i 3329
 
3.5%
Other values (29) 29092
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71027
75.2%
Space Separator 18131
 
19.2%
Decimal Number 2847
 
3.0%
Other Punctuation 2433
 
2.6%
Dash Punctuation 32
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7832
 
11.0%
r 6370
 
9.0%
e 6227
 
8.8%
o 5500
 
7.7%
s 4789
 
6.7%
t 4581
 
6.4%
g 4333
 
6.1%
n 4286
 
6.0%
i 3329
 
4.7%
l 3280
 
4.6%
Other values (16) 20500
28.9%
Decimal Number
ValueCountFrequency (%)
1 543
19.1%
3 527
18.5%
2 399
14.0%
4 370
13.0%
6 203
 
7.1%
5 200
 
7.0%
8 165
 
5.8%
7 162
 
5.7%
9 141
 
5.0%
0 137
 
4.8%
Space Separator
ValueCountFrequency (%)
18131
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2433
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71027
75.2%
Common 23443
 
24.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7832
 
11.0%
r 6370
 
9.0%
e 6227
 
8.8%
o 5500
 
7.7%
s 4789
 
6.7%
t 4581
 
6.4%
g 4333
 
6.1%
n 4286
 
6.0%
i 3329
 
4.7%
l 3280
 
4.6%
Other values (16) 20500
28.9%
Common
ValueCountFrequency (%)
18131
77.3%
, 2433
 
10.4%
1 543
 
2.3%
3 527
 
2.2%
2 399
 
1.7%
4 370
 
1.6%
6 203
 
0.9%
5 200
 
0.9%
8 165
 
0.7%
7 162
 
0.7%
Other values (3) 310
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94470
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18131
19.2%
a 7832
 
8.3%
r 6370
 
6.7%
e 6227
 
6.6%
o 5500
 
5.8%
s 4789
 
5.1%
t 4581
 
4.8%
g 4333
 
4.6%
n 4286
 
4.5%
i 3329
 
3.5%
Other values (29) 29092
30.8%

price
Real number (ℝ)

High correlation 

Distinct653
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7088527
Minimum0.28
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-04-20T17:57:28.416794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile1.4
Q12.37
median3.75
Q37.47
95-th percentile16.25
Maximum75
Range74.72
Interquartile range (IQR)5.1

Descriptive statistics

Standard deviation5.2671663
Coefficient of variation (CV)0.92263132
Kurtosis15.155053
Mean5.7088527
Median Absolute Deviation (MAD)1.78
Skewness2.7780263
Sum21048.54
Variance27.743041
MonotonicityNot monotonic
2025-04-20T17:57:28.466272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 55
 
1.5%
3.5 48
 
1.3%
4.25 45
 
1.2%
1.9 44
 
1.2%
2 42
 
1.1%
2.4 40
 
1.1%
7.5 40
 
1.1%
2.3 39
 
1.1%
8.5 38
 
1.0%
6.5 37
 
1.0%
Other values (643) 3259
88.4%
ValueCountFrequency (%)
0.28 1
 
< 0.1%
0.3 1
 
< 0.1%
0.43 1
 
< 0.1%
0.48 1
 
< 0.1%
0.54 1
 
< 0.1%
0.55 1
 
< 0.1%
0.58 1
 
< 0.1%
0.59 2
0.1%
0.6 1
 
< 0.1%
0.61 3
0.1%
ValueCountFrequency (%)
75 1
 
< 0.1%
50 2
0.1%
42 1
 
< 0.1%
40 1
 
< 0.1%
38 1
 
< 0.1%
35 2
0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
30 4
0.1%
29 2
0.1%

sector
Text

Distinct128
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size210.0 KiB
2025-04-20T17:57:28.538896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length31
Median length9
Mean length9.2763765
Min length8

Characters and Unicode

Total characters34202
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)0.7%

Sample

1st rowsector 1
2nd rowsector 107
3rd rowsector 33
4th rowsector 45
5th rowsector 50
ValueCountFrequency (%)
sector 3679
49.2%
65 165
 
2.2%
43 140
 
1.9%
102 139
 
1.9%
50 118
 
1.6%
109 114
 
1.5%
81 112
 
1.5%
23 98
 
1.3%
26 98
 
1.3%
66 94
 
1.3%
Other values (114) 2726
36.4%
2025-04-20T17:57:28.662259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3796
11.1%
c 3743
10.9%
o 3737
10.9%
e 3719
10.9%
t 3704
10.8%
s 3703
10.8%
r 3700
10.8%
1 1173
 
3.4%
6 952
 
2.8%
0 896
 
2.6%
Other values (24) 5079
14.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22508
65.8%
Decimal Number 7889
 
23.1%
Space Separator 3796
 
11.1%
Dash Punctuation 5
 
< 0.1%
Other Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 3743
16.6%
o 3737
16.6%
e 3719
16.5%
t 3704
16.5%
s 3703
16.5%
r 3700
16.4%
k 47
 
0.2%
b 41
 
0.2%
l 34
 
0.2%
a 18
 
0.1%
Other values (11) 62
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 1173
14.9%
6 952
12.1%
0 896
11.4%
5 781
9.9%
2 761
9.6%
8 741
9.4%
4 734
9.3%
3 645
8.2%
7 606
7.7%
9 600
7.6%
Space Separator
ValueCountFrequency (%)
3796
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22508
65.8%
Common 11694
34.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 3743
16.6%
o 3737
16.6%
e 3719
16.5%
t 3704
16.5%
s 3703
16.5%
r 3700
16.4%
k 47
 
0.2%
b 41
 
0.2%
l 34
 
0.2%
a 18
 
0.1%
Other values (11) 62
 
0.3%
Common
ValueCountFrequency (%)
3796
32.5%
1 1173
 
10.0%
6 952
 
8.1%
0 896
 
7.7%
5 781
 
6.7%
2 761
 
6.5%
8 741
 
6.3%
4 734
 
6.3%
3 645
 
5.5%
7 606
 
5.2%
Other values (3) 609
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3796
11.1%
c 3743
10.9%
o 3737
10.9%
e 3719
10.9%
t 3704
10.8%
s 3703
10.8%
r 3700
10.8%
1 1173
 
3.4%
6 952
 
2.8%
0 896
 
2.6%
Other values (24) 5079
14.9%

pricePerSqft
Real number (ℝ)

High correlation  Missing 

Distinct2492
Distinct (%)74.8%
Missing357
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean137493.5
Minimum4074.07
Maximum9166666.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-04-20T17:57:28.719305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4074.07
5-th percentile9149.633
Q112222.22
median18449.845
Q3250000
95-th percentile416666.67
Maximum9166666.7
Range9162592.6
Interquartile range (IQR)237777.78

Descriptive statistics

Standard deviation447431.98
Coefficient of variation (CV)3.2542047
Kurtosis319.83178
Mean137493.5
Median Absolute Deviation (MAD)8114.975
Skewness16.797717
Sum4.5785335 × 108
Variance2.0019538 × 1011
MonotonicityNot monotonic
2025-04-20T17:57:28.767876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250000 23
 
0.6%
300000 20
 
0.5%
375000 16
 
0.4%
350000 16
 
0.4%
400000 16
 
0.4%
10000 11
 
0.3%
360000 10
 
0.3%
319444.44 10
 
0.3%
333333.33 9
 
0.2%
416666.67 9
 
0.2%
Other values (2482) 3190
86.5%
(Missing) 357
 
9.7%
ValueCountFrequency (%)
4074.07 1
< 0.1%
5277.78 1
< 0.1%
5961.54 1
< 0.1%
6064.81 1
< 0.1%
6085.37 1
< 0.1%
6195.12 1
< 0.1%
6553.19 1
< 0.1%
6666.67 1
< 0.1%
6976.74 1
< 0.1%
7222.22 2
0.1%
ValueCountFrequency (%)
9166666.67 2
0.1%
9000000 2
0.1%
8750000 1
< 0.1%
8500000 1
< 0.1%
8000000 1
< 0.1%
7625000 1
< 0.1%
1062500 1
< 0.1%
957095.71 1
< 0.1%
805555.56 1
< 0.1%
777777.78 1
< 0.1%

propertyType
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size192.5 KiB
flat
2120 
house
1567 

Length

Max length5
Median length4
Mean length4.4250068
Min length4

Characters and Unicode

Total characters16315
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhouse
2nd rowflat
3rd rowflat
4th rowhouse
5th rowhouse

Common Values

ValueCountFrequency (%)
flat 2120
57.5%
house 1567
42.5%

Length

2025-04-20T17:57:28.811339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T17:57:28.852586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
flat 2120
57.5%
house 1567
42.5%

Most occurring characters

ValueCountFrequency (%)
f 2120
13.0%
l 2120
13.0%
a 2120
13.0%
t 2120
13.0%
h 1567
9.6%
o 1567
9.6%
u 1567
9.6%
s 1567
9.6%
e 1567
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 16315
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2120
13.0%
l 2120
13.0%
a 2120
13.0%
t 2120
13.0%
h 1567
9.6%
o 1567
9.6%
u 1567
9.6%
s 1567
9.6%
e 1567
9.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 16315
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2120
13.0%
l 2120
13.0%
a 2120
13.0%
t 2120
13.0%
h 1567
9.6%
o 1567
9.6%
u 1567
9.6%
s 1567
9.6%
e 1567
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16315
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2120
13.0%
l 2120
13.0%
a 2120
13.0%
t 2120
13.0%
h 1567
9.6%
o 1567
9.6%
u 1567
9.6%
s 1567
9.6%
e 1567
9.6%

area
Real number (ℝ)

High correlation  Missing 

Distinct811
Distinct (%)24.4%
Missing357
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean1653.0113
Minimum3
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-04-20T17:57:28.894474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile120
Q1360
median1707
Q32290
95-th percentile3850
Maximum20000
Range19997
Interquartile range (IQR)1930

Descriptive statistics

Standard deviation1377.918
Coefficient of variation (CV)0.83358048
Kurtosis13.897931
Mean1653.0113
Median Absolute Deviation (MAD)938
Skewness2.1452981
Sum5504527.6
Variance1898657.9
MonotonicityNot monotonic
2025-04-20T17:57:28.946565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360 84
 
2.3%
500 72
 
2.0%
300 68
 
1.8%
100 56
 
1.5%
60 52
 
1.4%
502 51
 
1.4%
263 48
 
1.3%
240 40
 
1.1%
215 38
 
1.0%
2400 32
 
0.9%
Other values (801) 2789
75.6%
(Missing) 357
 
9.7%
ValueCountFrequency (%)
3 2
 
0.1%
4 3
 
0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
55 2
 
0.1%
60 52
1.4%
61 2
 
0.1%
62 1
 
< 0.1%
65 1
 
< 0.1%
ValueCountFrequency (%)
20000 1
 
< 0.1%
12718 1
 
< 0.1%
9500 1
 
< 0.1%
9000 1
 
< 0.1%
8515 2
 
0.1%
8500 8
0.2%
8200 1
 
< 0.1%
7800 2
 
0.1%
7626 1
 
< 0.1%
7600 1
 
< 0.1%

superbuilt
Real number (ℝ)

High correlation  Missing 

Distinct919
Distinct (%)27.6%
Missing357
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean1760.9874
Minimum3.75
Maximum26000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-04-20T17:57:28.998100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3.75
5-th percentile156
Q1468
median1742
Q32333.25
95-th percentile4160
Maximum26000
Range25996.25
Interquartile range (IQR)1865.25

Descriptive statistics

Standard deviation1560.5364
Coefficient of variation (CV)0.88617124
Kurtosis25.124894
Mean1760.9874
Median Absolute Deviation (MAD)918
Skewness3.1520392
Sum5864088.2
Variance2435273.9
MonotonicityNot monotonic
2025-04-20T17:57:29.047811image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
468 80
 
2.2%
650 69
 
1.9%
390 62
 
1.7%
652.6 50
 
1.4%
341.9 41
 
1.1%
78 37
 
1.0%
130 33
 
0.9%
2400 32
 
0.9%
279.5 31
 
0.8%
312 30
 
0.8%
Other values (909) 2865
77.7%
(Missing) 357
 
9.7%
ValueCountFrequency (%)
3.75 1
 
< 0.1%
3.9 1
 
< 0.1%
5 2
0.1%
5.2 1
 
< 0.1%
7.8 1
 
< 0.1%
10.4 1
 
< 0.1%
12.5 1
 
< 0.1%
68.75 1
 
< 0.1%
71.5 1
 
< 0.1%
72 3
0.1%
ValueCountFrequency (%)
26000 1
 
< 0.1%
12718 1
 
< 0.1%
12350 1
 
< 0.1%
11700 1
 
< 0.1%
11050 8
0.2%
10660 1
 
< 0.1%
10140 2
 
0.1%
9913.8 1
 
< 0.1%
9880 1
 
< 0.1%
9750 1
 
< 0.1%

carpet
Real number (ℝ)

High correlation  Missing 

Distinct980
Distinct (%)29.4%
Missing357
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean1161.2127
Minimum2.4
Maximum17000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-04-20T17:57:29.094556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.4
5-th percentile102
Q1306
median1127.75
Q31532.7
95-th percentile2775.01
Maximum17000
Range16997.6
Interquartile range (IQR)1226.7

Descriptive statistics

Standard deviation1046.6036
Coefficient of variation (CV)0.90130223
Kurtosis22.933786
Mean1161.2127
Median Absolute Deviation (MAD)622.125
Skewness3.0352403
Sum3866838.4
Variance1095379.1
MonotonicityNot monotonic
2025-04-20T17:57:29.144284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306 80
 
2.2%
425 69
 
1.9%
255 62
 
1.7%
426.7 50
 
1.4%
223.55 41
 
1.1%
51 37
 
1.0%
85 33
 
0.9%
1680 31
 
0.8%
182.75 31
 
0.8%
204 30
 
0.8%
Other values (970) 2866
77.7%
(Missing) 357
 
9.7%
ValueCountFrequency (%)
2.4 1
 
< 0.1%
2.55 1
 
< 0.1%
3.2 2
0.1%
3.4 1
 
< 0.1%
5.1 1
 
< 0.1%
6.8 1
 
< 0.1%
8 1
 
< 0.1%
44 1
 
< 0.1%
45 3
0.1%
45.75 1
 
< 0.1%
ValueCountFrequency (%)
17000 1
 
< 0.1%
8902.6 1
 
< 0.1%
8075 1
 
< 0.1%
7650 1
 
< 0.1%
7225 8
0.2%
6970 1
 
< 0.1%
6630 2
 
0.1%
6482.1 1
 
< 0.1%
6460 1
 
< 0.1%
6375 1
 
< 0.1%

builtup
Real number (ℝ)

High correlation  Missing 

Distinct881
Distinct (%)26.5%
Missing357
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean1411.48
Minimum3
Maximum20000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-04-20T17:57:29.194869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile120
Q1360
median1388
Q31874.4
95-th percentile3300
Maximum20000
Range19997
Interquartile range (IQR)1514.4

Descriptive statistics

Standard deviation1250.7737
Coefficient of variation (CV)0.88614343
Kurtosis21.297299
Mean1411.48
Median Absolute Deviation (MAD)772
Skewness2.880264
Sum4700228.5
Variance1564435
MonotonicityNot monotonic
2025-04-20T17:57:29.246242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
360 84
 
2.3%
500 72
 
2.0%
300 68
 
1.8%
100 56
 
1.5%
60 52
 
1.4%
502 51
 
1.4%
263 48
 
1.3%
2040 46
 
1.2%
240 40
 
1.1%
215 38
 
1.0%
Other values (871) 2775
75.3%
(Missing) 357
 
9.7%
ValueCountFrequency (%)
3 2
 
0.1%
4 3
 
0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
55 2
 
0.1%
60 52
1.4%
61 2
 
0.1%
62 1
 
< 0.1%
65 1
 
< 0.1%
ValueCountFrequency (%)
20000 1
 
< 0.1%
10810.3 1
 
< 0.1%
9500 1
 
< 0.1%
9000 1
 
< 0.1%
8500 8
0.2%
8200 1
 
< 0.1%
7800 2
 
0.1%
7626 1
 
< 0.1%
7600 1
 
< 0.1%
7500 1
 
< 0.1%

bedRooms
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2365066
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-04-20T17:57:29.293499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q35
95-th percentile9
Maximum49
Range48
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8648224
Coefficient of variation (CV)0.6762228
Kurtosis47.734116
Mean4.2365066
Median Absolute Deviation (MAD)1
Skewness5.3349426
Sum15620
Variance8.2072073
MonotonicityNot monotonic
2025-04-20T17:57:29.337080image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
3 1456
39.5%
4 881
23.9%
2 407
 
11.0%
5 378
 
10.3%
6 216
 
5.9%
9 74
 
2.0%
7 68
 
1.8%
8 47
 
1.3%
10 40
 
1.1%
12 38
 
1.0%
Other values (19) 82
 
2.2%
ValueCountFrequency (%)
1 7
 
0.2%
2 407
 
11.0%
3 1456
39.5%
4 881
23.9%
5 378
 
10.3%
6 216
 
5.9%
7 68
 
1.8%
8 47
 
1.3%
9 74
 
2.0%
10 40
 
1.1%
ValueCountFrequency (%)
49 1
 
< 0.1%
39 1
 
< 0.1%
36 3
0.1%
32 2
 
0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
24 3
0.1%
23 1
 
< 0.1%
22 5
0.1%
20 7
0.2%

bathRooms
Real number (ℝ)

High correlation 

Distinct32
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5033903
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-04-20T17:57:29.378727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile9
Maximum55
Range54
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.0004389
Coefficient of variation (CV)0.66626224
Kurtosis54.167914
Mean4.5033903
Median Absolute Deviation (MAD)1
Skewness5.4370967
Sum16604
Variance9.0026336
MonotonicityNot monotonic
2025-04-20T17:57:29.422863image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3 1040
28.2%
4 970
26.3%
2 457
12.4%
5 402
 
10.9%
6 362
 
9.8%
7 146
 
4.0%
9 74
 
2.0%
8 64
 
1.7%
10 52
 
1.4%
12 30
 
0.8%
Other values (22) 90
 
2.4%
ValueCountFrequency (%)
1 13
 
0.4%
2 457
12.4%
3 1040
28.2%
4 970
26.3%
5 402
 
10.9%
6 362
 
9.8%
7 146
 
4.0%
8 64
 
1.7%
9 74
 
2.0%
10 52
 
1.4%
ValueCountFrequency (%)
55 1
 
< 0.1%
43 1
 
< 0.1%
40 2
0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
27 1
 
< 0.1%
26 1
 
< 0.1%
25 1
 
< 0.1%
24 3
0.1%
Distinct672
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Memory size305.8 KiB
2025-04-20T17:57:29.515388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length70
Median length60
Mean length35.897206
Min length18

Characters and Unicode

Total characters132353
Distinct characters64
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique298 ?
Unique (%)8.1%

Sample

1st row palam vihar, gurgaon
2nd rowM3M Woodshire Sector 107, Gurgaon
3rd rowCentral Park Flower Valley Aqua Front Towers Sector-33 Sohna, Gurgaon
4th row sector 45, gurgaon
5th row nirvana country, gurgaon
ValueCountFrequency (%)
gurgaon 3742
 
17.9%
sector 2884
 
13.8%
dlf 551
 
2.6%
phase 549
 
2.6%
the 379
 
1.8%
city 367
 
1.8%
1 352
 
1.7%
park 241
 
1.2%
sushant 222
 
1.1%
block 221
 
1.1%
Other values (644) 11377
54.5%
2025-04-20T17:57:29.786452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
20335
15.4%
r 10489
 
7.9%
a 9785
 
7.4%
o 9641
 
7.3%
e 7852
 
5.9%
n 6645
 
5.0%
g 5916
 
4.5%
t 5906
 
4.5%
u 5147
 
3.9%
, 4556
 
3.4%
Other values (54) 46081
34.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 88054
66.5%
Space Separator 20335
 
15.4%
Uppercase Letter 11846
 
9.0%
Decimal Number 7513
 
5.7%
Other Punctuation 4556
 
3.4%
Dash Punctuation 49
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 10489
11.9%
a 9785
11.1%
o 9641
10.9%
e 7852
8.9%
n 6645
 
7.5%
g 5916
 
6.7%
t 5906
 
6.7%
u 5147
 
5.8%
c 4389
 
5.0%
s 4254
 
4.8%
Other values (16) 18030
20.5%
Uppercase Letter
ValueCountFrequency (%)
G 2657
22.4%
S 2629
22.2%
P 815
 
6.9%
T 788
 
6.7%
M 773
 
6.5%
A 508
 
4.3%
E 450
 
3.8%
C 416
 
3.5%
F 366
 
3.1%
D 362
 
3.1%
Other values (15) 2082
17.6%
Decimal Number
ValueCountFrequency (%)
1 1416
18.8%
6 866
11.5%
3 805
10.7%
0 778
10.4%
2 730
9.7%
8 711
9.5%
9 577
7.7%
4 559
 
7.4%
7 548
 
7.3%
5 523
 
7.0%
Space Separator
ValueCountFrequency (%)
20335
100.0%
Other Punctuation
ValueCountFrequency (%)
, 4556
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 99900
75.5%
Common 32453
 
24.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 10489
 
10.5%
a 9785
 
9.8%
o 9641
 
9.7%
e 7852
 
7.9%
n 6645
 
6.7%
g 5916
 
5.9%
t 5906
 
5.9%
u 5147
 
5.2%
c 4389
 
4.4%
s 4254
 
4.3%
Other values (41) 29876
29.9%
Common
ValueCountFrequency (%)
20335
62.7%
, 4556
 
14.0%
1 1416
 
4.4%
6 866
 
2.7%
3 805
 
2.5%
0 778
 
2.4%
2 730
 
2.2%
8 711
 
2.2%
9 577
 
1.8%
4 559
 
1.7%
Other values (3) 1120
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 132353
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20335
15.4%
r 10489
 
7.9%
a 9785
 
7.4%
o 9641
 
7.3%
e 7852
 
5.9%
n 6645
 
5.0%
g 5916
 
4.5%
t 5906
 
4.5%
u 5147
 
3.9%
, 4556
 
3.4%
Other values (54) 46081
34.8%

floorNum
Real number (ℝ)

High correlation 

Distinct42
Distinct (%)1.1%
Missing12
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean6.8307483
Minimum-1
Maximum46
Zeros34
Zeros (%)0.9%
Negative1
Negative (%)< 0.1%
Memory size28.9 KiB
2025-04-20T17:57:29.844453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q13
median4
Q310
95-th percentile19
Maximum46
Range47
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.9071643
Coefficient of variation (CV)0.86479022
Kurtosis3.9840106
Mean6.8307483
Median Absolute Deviation (MAD)2
Skewness1.7768914
Sum25103
Variance34.89459
MonotonicityNot monotonic
2025-04-20T17:57:29.890925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
3 747
20.3%
2 554
15.0%
4 368
10.0%
5 225
 
6.1%
10 195
 
5.3%
8 193
 
5.2%
7 180
 
4.9%
1 159
 
4.3%
6 151
 
4.1%
12 137
 
3.7%
Other values (32) 766
20.8%
ValueCountFrequency (%)
-1 1
 
< 0.1%
0 34
 
0.9%
1 159
 
4.3%
2 554
15.0%
3 747
20.3%
4 368
10.0%
5 225
 
6.1%
6 151
 
4.1%
7 180
 
4.9%
8 193
 
5.2%
ValueCountFrequency (%)
46 1
 
< 0.1%
45 1
 
< 0.1%
38 1
 
< 0.1%
37 1
 
< 0.1%
36 2
 
0.1%
35 3
0.1%
34 3
0.1%
33 2
 
0.1%
32 3
0.1%
31 6
0.2%

facing
Categorical

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size200.5 KiB
north-east
1062 
east
940 
north
623 
west
238 
north-west
227 
Other values (4)
597 

Length

Max length10
Median length5
Mean length6.6566314
Min length2

Characters and Unicode

Total characters24543
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownorth
2nd roweast
3rd rownorth
4th roweast
5th rownorth-east

Common Values

ValueCountFrequency (%)
north-east 1062
28.8%
east 940
25.5%
north 623
16.9%
west 238
 
6.5%
north-west 227
 
6.2%
na 173
 
4.7%
south 152
 
4.1%
south-east 150
 
4.1%
south-west 122
 
3.3%

Length

2025-04-20T17:57:29.937896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T17:57:29.984060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
north-east 1062
28.8%
east 940
25.5%
north 623
16.9%
west 238
 
6.5%
north-west 227
 
6.2%
na 173
 
4.7%
south 152
 
4.1%
south-east 150
 
4.1%
south-west 122
 
3.3%

Most occurring characters

ValueCountFrequency (%)
t 5075
20.7%
s 3163
12.9%
e 2739
11.2%
o 2336
9.5%
h 2336
9.5%
a 2325
9.5%
n 2085
8.5%
r 1912
 
7.8%
- 1561
 
6.4%
w 587
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22982
93.6%
Dash Punctuation 1561
 
6.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 5075
22.1%
s 3163
13.8%
e 2739
11.9%
o 2336
10.2%
h 2336
10.2%
a 2325
10.1%
n 2085
9.1%
r 1912
 
8.3%
w 587
 
2.6%
u 424
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 1561
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 22982
93.6%
Common 1561
 
6.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 5075
22.1%
s 3163
13.8%
e 2739
11.9%
o 2336
10.2%
h 2336
10.2%
a 2325
10.1%
n 2085
9.1%
r 1912
 
8.3%
w 587
 
2.6%
u 424
 
1.8%
Common
ValueCountFrequency (%)
- 1561
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24543
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 5075
20.7%
s 3163
12.9%
e 2739
11.2%
o 2336
9.5%
h 2336
9.5%
a 2325
9.5%
n 2085
8.5%
r 1912
 
7.8%
- 1561
 
6.4%
w 587
 
2.4%

agePossesion
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size248.5 KiB
relatively new property
1931 
moderatly old property
810 
new property
586 
old property
339 
under construction
 
21

Length

Max length23
Median length23
Mean length19.992135
Min length12

Characters and Unicode

Total characters73711
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrelatively new property
2nd rowrelatively new property
3rd rowrelatively new property
4th rownew property
5th rowrelatively new property

Common Values

ValueCountFrequency (%)
relatively new property 1931
52.4%
moderatly old property 810
22.0%
new property 586
 
15.9%
old property 339
 
9.2%
under construction 21
 
0.6%

Length

2025-04-20T17:57:30.033891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T17:57:30.074295image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
property 3666
36.2%
new 2517
24.9%
relatively 1931
19.1%
old 1149
 
11.4%
moderatly 810
 
8.0%
under 21
 
0.2%
construction 21
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 10876
14.8%
r 10115
13.7%
p 7332
9.9%
t 6449
8.7%
6428
8.7%
y 6407
8.7%
l 5821
7.9%
o 5667
7.7%
a 2741
 
3.7%
n 2580
 
3.5%
Other values (8) 9295
12.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 67283
91.3%
Space Separator 6428
 
8.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10876
16.2%
r 10115
15.0%
p 7332
10.9%
t 6449
9.6%
y 6407
9.5%
l 5821
8.7%
o 5667
8.4%
a 2741
 
4.1%
n 2580
 
3.8%
w 2517
 
3.7%
Other values (7) 6778
10.1%
Space Separator
ValueCountFrequency (%)
6428
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 67283
91.3%
Common 6428
 
8.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10876
16.2%
r 10115
15.0%
p 7332
10.9%
t 6449
9.6%
y 6407
9.5%
l 5821
8.7%
o 5667
8.4%
a 2741
 
4.1%
n 2580
 
3.8%
w 2517
 
3.7%
Other values (7) 6778
10.1%
Common
ValueCountFrequency (%)
6428
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73711
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10876
14.8%
r 10115
13.7%
p 7332
9.9%
t 6449
8.7%
6428
8.7%
y 6407
8.7%
l 5821
7.9%
o 5667
7.7%
a 2741
 
3.7%
n 2580
 
3.5%
Other values (8) 9295
12.6%

balcony
Categorical

High correlation  Missing 

Distinct5
Distinct (%)0.3%
Missing2120
Missing (%)57.5%
Memory size193.4 KiB
3+
843 
3
347 
2
325 
1
 
42
0
 
10

Length

Max length2
Median length2
Mean length1.5379706
Min length1

Characters and Unicode

Total characters2410
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3+
3rd row2
4th row3+
5th row2

Common Values

ValueCountFrequency (%)
3+ 843
 
22.9%
3 347
 
9.4%
2 325
 
8.8%
1 42
 
1.1%
0 10
 
0.3%
(Missing) 2120
57.5%

Length

2025-04-20T17:57:30.120349image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T17:57:30.159211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
3 1190
75.9%
2 325
 
20.7%
1 42
 
2.7%
0 10
 
0.6%

Most occurring characters

ValueCountFrequency (%)
3 1190
49.4%
+ 843
35.0%
2 325
 
13.5%
1 42
 
1.7%
0 10
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1567
65.0%
Math Symbol 843
35.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1190
75.9%
2 325
 
20.7%
1 42
 
2.7%
0 10
 
0.6%
Math Symbol
ValueCountFrequency (%)
+ 843
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2410
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 1190
49.4%
+ 843
35.0%
2 325
 
13.5%
1 42
 
1.7%
0 10
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 1190
49.4%
+ 843
35.0%
2 325
 
13.5%
1 42
 
1.7%
0 10
 
0.4%

pooja room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
0
2503 
1
1184 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 2503
67.9%
1 1184
32.1%

Length

2025-04-20T17:57:30.201758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T17:57:30.237496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2503
67.9%
1 1184
32.1%

Most occurring characters

ValueCountFrequency (%)
0 2503
67.9%
1 1184
32.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2503
67.9%
1 1184
32.1%

Most occurring scripts

ValueCountFrequency (%)
Common 3687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2503
67.9%
1 1184
32.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2503
67.9%
1 1184
32.1%

servant room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
1
2188 
0
1499 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 2188
59.3%
0 1499
40.7%

Length

2025-04-20T17:57:30.275329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T17:57:30.311142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2188
59.3%
0 1499
40.7%

Most occurring characters

ValueCountFrequency (%)
1 2188
59.3%
0 1499
40.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2188
59.3%
0 1499
40.7%

Most occurring scripts

ValueCountFrequency (%)
Common 3687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2188
59.3%
0 1499
40.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2188
59.3%
0 1499
40.7%

study room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
0
2550 
1
1137 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 2550
69.2%
1 1137
30.8%

Length

2025-04-20T17:57:30.349011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T17:57:30.384074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2550
69.2%
1 1137
30.8%

Most occurring characters

ValueCountFrequency (%)
0 2550
69.2%
1 1137
30.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2550
69.2%
1 1137
30.8%

Most occurring scripts

ValueCountFrequency (%)
Common 3687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2550
69.2%
1 1137
30.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2550
69.2%
1 1137
30.8%

others
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
0
3300 
1
387 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 3300
89.5%
1 387
 
10.5%

Length

2025-04-20T17:57:30.422728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T17:57:30.457899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 3300
89.5%
1 387
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 3300
89.5%
1 387
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3300
89.5%
1 387
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 3687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3300
89.5%
1 387
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3300
89.5%
1 387
 
10.5%

store room
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
0
2897 
1
790 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3687
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2897
78.6%
1 790
 
21.4%

Length

2025-04-20T17:57:30.496262image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T17:57:30.531307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2897
78.6%
1 790
 
21.4%

Most occurring characters

ValueCountFrequency (%)
0 2897
78.6%
1 790
 
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2897
78.6%
1 790
 
21.4%

Most occurring scripts

ValueCountFrequency (%)
Common 3687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2897
78.6%
1 790
 
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2897
78.6%
1 790
 
21.4%

furnishType
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size180.2 KiB
1
1822 
2
1487 
0
378 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3687
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 1822
49.4%
2 1487
40.3%
0 378
 
10.3%

Length

2025-04-20T17:57:30.568959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-20T17:57:30.606179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1822
49.4%
2 1487
40.3%
0 378
 
10.3%

Most occurring characters

ValueCountFrequency (%)
1 1822
49.4%
2 1487
40.3%
0 378
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3687
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1822
49.4%
2 1487
40.3%
0 378
 
10.3%

Most occurring scripts

ValueCountFrequency (%)
Common 3687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1822
49.4%
2 1487
40.3%
0 378
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1822
49.4%
2 1487
40.3%
0 378
 
10.3%

luxuryScore
Real number (ℝ)

Distinct204
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.73827
Minimum0
Maximum221
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size28.9 KiB
2025-04-20T17:57:30.649663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33
Q180
median127
Q3190
95-th percentile221
Maximum221
Range221
Interquartile range (IQR)110

Descriptive statistics

Standard deviation61.814419
Coefficient of variation (CV)0.47281044
Kurtosis-1.2011258
Mean130.73827
Median Absolute Deviation (MAD)55
Skewness-0.066606305
Sum482032
Variance3821.0224
MonotonicityNot monotonic
2025-04-20T17:57:30.696100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
221 389
 
10.6%
63 195
 
5.3%
207 116
 
3.1%
94 109
 
3.0%
110 99
 
2.7%
87 92
 
2.5%
199 76
 
2.1%
190 58
 
1.6%
203 47
 
1.3%
103 43
 
1.2%
Other values (194) 2463
66.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
6 8
0.2%
7 15
0.4%
8 11
0.3%
10 7
0.2%
13 8
0.2%
14 7
0.2%
15 17
0.5%
16 2
 
0.1%
17 3
 
0.1%
ValueCountFrequency (%)
221 389
10.6%
215 1
 
< 0.1%
214 19
 
0.5%
213 12
 
0.3%
212 22
 
0.6%
211 18
 
0.5%
208 3
 
0.1%
207 116
 
3.1%
206 12
 
0.3%
205 11
 
0.3%

Interactions

2025-04-20T17:57:27.477568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:23.913274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.467374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.822270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.200261image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.562416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.935420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.313472image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.669188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.130185image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.513598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:23.970595image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.503153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.862339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.239505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.601327image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.975397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.350135image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.705626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.166152image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.546782image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.037950image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.536427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.897963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.273415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.637049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.011398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.383735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.740064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.199169image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.584762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.109711image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.574992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.938386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.313267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.676574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.052335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.422492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.778643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.237488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.619336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.247240image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.611883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.975250image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.347836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.713601image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.089284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.457417image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.813780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.271211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.655794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.286388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.648874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.015061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.384985image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.751102image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.128755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.495931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.850785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.308479image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.694355image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.327041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.687968image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.055151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.424120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.791955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.167839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.534329image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.889907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.346002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.727298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.362501image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.722053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.092181image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.458718image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.828620image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.206049image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.568925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.923600image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.380040image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.760429image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.398603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.756389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.128964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.494681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.865516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.242247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.603196image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.957461image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.413710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.793136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.432634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:24.789320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.164781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.527707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:25.900147image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.277841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:26.636000image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.095958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-04-20T17:57:27.445764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-04-20T17:57:30.735385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
agePossesionareabalconybathRoomsbedRoomsbuiltupcarpetfacingfloorNumfurnishTypeluxuryScoreotherspooja roompricepricePerSqftpropertyTypeservant roomstore roomstudy roomsuperbuilt
agePossesion1.0000.0740.0780.0490.0860.0560.0480.0990.1830.1160.1530.0000.1140.0820.0530.3190.0270.0640.0500.045
area0.0741.0000.052-0.042-0.1890.9960.9950.0000.4910.1080.3380.0740.228-0.078-0.6000.4110.4310.2210.2140.995
balcony0.0780.0521.0000.1470.1610.0520.0520.0700.0980.2160.1600.0740.1440.1360.0001.0000.2660.1440.2450.052
bathRooms0.049-0.0420.1471.0000.891-0.022-0.0140.035-0.2520.193-0.0350.1050.2270.6940.4920.4040.0620.2010.172-0.021
bedRooms0.086-0.1890.1610.8911.000-0.161-0.1510.044-0.3620.188-0.1300.1120.2540.7100.5830.4860.0270.2060.169-0.161
builtup0.0560.9960.052-0.022-0.1611.0001.0000.0000.4670.1110.3230.0500.157-0.066-0.5900.2540.3580.1530.1760.999
carpet0.0480.9950.052-0.014-0.1511.0001.0000.0000.4650.1180.3230.0380.157-0.062-0.5870.2000.3130.1370.1770.999
facing0.0990.0000.0700.0350.0440.0000.0001.0000.0440.0900.0950.0340.0560.0370.0210.1230.0400.0880.0000.008
floorNum0.1830.4910.098-0.252-0.3620.4670.4650.0441.0000.0790.2490.0520.317-0.350-0.4880.6710.0550.2820.2240.466
furnishType0.1160.1080.2160.1930.1880.1110.1180.0900.0791.0000.1910.0740.1430.1510.0000.0990.1870.1360.1550.114
luxuryScore0.1530.3380.160-0.035-0.1300.3230.3230.0950.2490.1911.0000.2680.125-0.011-0.2160.3960.2200.1500.1520.324
others0.0000.0740.0740.1050.1120.0500.0380.0340.0520.0740.2681.0000.0720.0840.0130.0950.0650.1750.0750.049
pooja room0.1140.2280.1440.2270.2540.1570.1570.0560.3170.1430.1250.0721.0000.4150.0180.4330.2000.3860.4280.157
price0.082-0.0780.1360.6940.710-0.066-0.0620.037-0.3500.151-0.0110.0840.4151.0000.7650.5300.3270.3460.356-0.061
pricePerSqft0.053-0.6000.0000.4920.583-0.590-0.5870.021-0.4880.000-0.2160.0130.0180.7651.0000.0570.0410.0000.016-0.585
propertyType0.3190.4111.0000.4040.4860.2540.2000.1230.6710.0990.3960.0950.4330.5300.0571.0000.0680.3740.2870.202
servant room0.0270.4310.2660.0620.0270.3580.3130.0400.0550.1870.2200.0650.2000.3270.0410.0681.0000.1450.2020.309
store room0.0640.2210.1440.2010.2060.1530.1370.0880.2820.1360.1500.1750.3860.3460.0000.3740.1451.0000.3370.137
study room0.0500.2140.2450.1720.1690.1760.1770.0000.2240.1550.1520.0750.4280.3560.0160.2870.2020.3371.0000.176
superbuilt0.0450.9950.052-0.021-0.1610.9990.9990.0080.4660.1140.3240.0490.157-0.061-0.5850.2020.3090.1370.1761.000

Missing values

2025-04-20T17:57:27.849838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-20T17:57:27.964398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-20T17:57:28.048467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

societypricesectorpricePerSqftpropertyTypeareasuperbuiltcarpetbuiltupbedRoomsbathRoomsaddressfloorNumfacingagePossesionbalconypooja roomservant roomstudy roomothersstore roomfurnishTypeluxuryScore
0palam vihar, gurgaon4.26sector 124027.07house1773.02304.91507.051773.06.06.0palam vihar, gurgaon3.0northrelatively new property300000158
1m3m woodshire1.70sector 1078749.36flat1943.01943.01262.951554.43.03.0M3M Woodshire Sector 107, Gurgaon7.0eastrelatively new propertyNaN00000177
2central park flower valley aqua front towers2.50sector 3313974.29flat1789.01789.01162.851431.23.03.0Central Park Flower Valley Aqua Front Towers Sector-33 Sohna, Gurgaon5.0northrelatively new propertyNaN000012131
3sector 45, gurgaon8.30sector 4544647.66house1859.02416.71580.151859.012.012.0sector 45, gurgaon4.0eastnew property3+101000198
4nirvana country, gurgaon10.40sector 50433333.33house240.0312.0204.00240.04.04.0nirvana country, gurgaonNaNnorth-eastrelatively new property201100137
5vatika signature villas, sector 82, gurgaon6.70sector 82186111.11house360.0468.0306.00360.04.04.0vatika signature villas, sector 82, gurgaon2.0westrelatively new property3+11101288
6sector 46, gurgaon3.70sector 46370000.00house100.0125.080.00100.03.03.0sector 46, gurgaon1.0north-westnew property211101199
7bptp mansions park prime3.90sector 6614109.99flat2764.02764.01934.802349.44.04.0BPTP Mansions Park Prime Sector 66, Gurgaon6.0southrelatively new propertyNaN01010213
8tata primanti4.84sector 7218980.39flat2550.02550.01657.502040.03.03.0Tata Primanti Sector 72, Gurgaon5.0north-eastnew propertyNaN000001221
9sushant lok phase 1, gurgaon8.00sector 43372093.02house215.0279.5182.75215.05.06.0sushant lok phase 1, gurgaon2.0north-eastrelatively new property2110001199
societypricesectorpricePerSqftpropertyTypeareasuperbuiltcarpetbuiltupbedRoomsbathRoomsaddressfloorNumfacingagePossesionbalconypooja roomservant roomstudy roomothersstore roomfurnishTypeluxuryScore
3677m3m skycity3.81sector 6518549.17flat2054.02054.01335.101643.203.03.0M3M Skycity Sector 65, Gurgaon21.0eastnew propertyNaN010000203
3678block d sector 56, gurgaon18.00block d sector 56360000.00house500.0650.0425.00500.0016.016.0block d sector 56, gurgaon4.0north-eastrelatively new property3+101001207
3679puri diplomatic greens4.80sector 11116271.19flat2950.02950.02065.002507.504.05.0Puri Diplomatic Greens Sector 111, Gurgaon12.0north-westrelatively new propertyNaN01000187
3680godrej oasis2.15sector 8811621.62flat1850.01850.01202.501480.003.03.0Godrej Oasis Sector 88A, Gurgaon12.0eastrelatively new propertyNaN000012117
3681emaar digihomes3.55sector 6223541.11flat1508.01508.0904.801131.002.02.0Emaar Digihomes Sector 62, Gurgaon15.0north-eastrelatively new propertyNaN000002172
3682independent floor at dlf city, v block dlf phase 3, gurgaon5.30sector 2336128.15house1467.01907.11246.951467.004.05.0independent floor at dlf city, v block dlf phase 3, gurgaon2.0north-eastrelatively new property3+000011166
3683emaar the palm sprgs, sector 54, gurgaon26.00sector 54722222.22house360.0468.0306.00360.005.06.0emaar the palm sprgs, sector 54, gurgaon2.0north-eastmoderatly old property3+01000153
3684tata primanti, sector 72, gurgaon14.00sector 72NaNhouseNaNNaNNaNNaN5.05.0tata primanti, sector 72, gurgaon3.0narelatively new property3+101001125
3685m3m golfestate6.80sector 6517444.84flat3898.03898.02533.703118.403.03.0M3M Golfestate Sector 65, Gurgaon11.0north-eastrelatively new propertyNaN011002221
3686sobha city2.60sector 10818826.94flat1381.01381.0828.601035.752.02.0Sobha City Sector 108, Gurgaon11.0westrelatively new propertyNaN000001204

Duplicate rows

Most frequently occurring

societypricesectorpricePerSqftpropertyTypeareasuperbuiltcarpetbuiltupbedRoomsbathRoomsaddressfloorNumfacingagePossesionbalconypooja roomservant roomstudy roomothersstore roomfurnishTypeluxuryScore# duplicates
0raheja revanta, sector 78, gurgaon4.25sector 78NaNhouseNaNNaNNaNNaN4.03.0raheja revanta, sector 78, gurgaon4.0eastnew property2010002763
1raheja revanta, sector 78, gurgaon4.25sector 78NaNhouseNaNNaNNaNNaN4.03.0raheja revanta, sector 78, gurgaon4.0eastnew property2010002943
2raheja revanta, sector 78, gurgaon4.25sector 78NaNhouseNaNNaNNaNNaN4.03.0raheja revanta, sector 78, gurgaon4.0nanew property2010002453
3tulip yellow2.39sector 6914025.82flat1704.01704.01107.61363.23.03.0Tulip Yellow Sector 69, Gurgaon3.0eastrelatively new propertyNaN0000022212